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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 154 Documents
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Social media and optimization of the promotion of Lake Toba tourism destinations in Indonesia Muhammad Said Harahap; Rizal Khadafi; Effiati Juliana Hasibuan; Agung Saputra; Sigit Hardiyanto; Faizal Hamzah Lubis
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p208-216

Abstract

Tourism is one of the largest contributors to Indonesia's foreign exchange earnings, surpassing taxation, energy, and gas. This study seeks to investigate the use of social media to optimize the promotion of Lake Toba as a tourist destination, which has been impacted by the COVID-19 pandemic. Using interview techniques and live field observations, it was discovered that social media, particularly the Instagram platform, play a significant role in promoting Lake Toba tourism. The Department of Culture and Tourism of the North Sumatra Province uses landscape photography as its primary promotion method, which has proved to be more effective and interesting than conventional methods such as the distribution of brochures or the use of manuals. The capture procedure and techniques for landscape photography were carried out by professional photographers in collaboration with the Department of Culture and Tourism of the North Sumatra Province. In addition to providing information, tourism_sumut's Instagram account functions as a platform to raise public awareness about Lake Toba tourism and as a promotional medium for North Sumatra's tourist attractions on an international scale. Department of Culture and Tourism of the North Sumatra Province collaborates with travel agencies and local communities to disseminate Lake Toba tourism information.
Designing a framework for blockchain-based e-voting system for Libya Salem S. M. Khalifa; Ali Mohamed E. Ejmaa; Abdulmawla Mohammad Ali Najih; Mohamed Abd Arahman Masoud Zneen
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p191-198

Abstract

A transition to democratic rule is considered the first step down a long road towards Libya’s recovery and prosperity. Thus, it strives to improve the country’s elections by introducing new technologies. A blockchain is a distributed ledger that is characterised by independence and security. Therefore, it has been widely applied in various fields ranging from credit encryption and digital currency. With the development of internet technology, electronic voting (E-voting) systems have been greatly popularised. However, they suffer from various security threats, which create a sense of distrust among existing systems. Integrating blockchain with online elections is a promising trend, which could lead to make an election transparent, immutable, reliable, and more secure. In this paper, we present a literature review and a case analysis of blockchain technology. Moreover, a framework for an E-voting system based on blockchain is proposed. The methodology is adopted on the basis of three activities, they are identification of the relevant literature about E-voting, system modelling, and the determination of suitable technological tools. The framework is secure and reliable. Thus, it could help increase the number of voters and ensure a high level of participation, as well as facilitate free and fair electoral processes
Hybrid transformation with 1’st order statistics for medical image analysis and classification Loay E. George; Maryam Yaseen Abdullah; Raghad K. Abdulhassan; Asmaa Abdulrazzaq Al_Qaisi
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p249-257

Abstract

Skin cancer, one of the most critical forms of cancer, required early detection and documentation for efficient treatment, especially as certain types are fatal. In this study, an artificial neural network (ANN) was utilized to discover and index diverse melanomas using the ISIC 2018 dataset. The pre-processing phase is stringent as it insulates the cancerous fraction of a skin image, involving removing, trimming, thinning, and normalizing. In this phase, unwanted hair pieces on the image are eliminated in this phase. Feature extraction from the clipped image is achieved using a discrete cosine transform (DCT) and a gradient transform to transform it into frequency-domain coefficients. Statistical feature extraction is used to reduce the amount of data required for ANN training. A dataset from ISIC 2018 that consists of seven different images from dermoscopic procedures for classification purposes is used in the empirical investigation. An accuracy of 85.44% for DCT in the sub-bands and 76.07% for the sub-band gradient transform was achieved by the applied ANN. The hybrid system's mean squared error (MSE) was discovered to be 3.52×10-4. The work highlights the potential of ANN in the early detection of skin cancer, supporting more efficient treatment and preventing advanced cases.
Generalization of linear and non-linear support vector machine in multiple fields: a review Sundas Naqeeb Khan; Samra Urooj Khan; Hanane Aznaoui; Canan Batur Şahin; Özlem Batur Dinler
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p226-239

Abstract

Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.
Exploring network security threats through text mining techniques: a comprehensive analysis Tri Wahyuningsih; Irwan Sembiring; Adi Setiawan; Iwan Setyawan
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p258-267

Abstract

In response to the escalating cybersecurity threats, this research focuses on leveraging text mining techniques to analyze network security data effectively. The study utilizes user-generated reports detailing attacks on server networks. Employing clustering algorithms, these reports are grouped based on threat levels. Additionally, a classification algorithm discerns whether network activities pose security risks. The research achieves a noteworthy 93% accuracy in text classification, showcasing the efficacy of these techniques. The novelty lies in classifying security threat report logs according to their threat levels. Prioritizing high-risk threats, this approach aids network management in strategic focus. By enabling swift identification and categorization of network security threats, this research equips organizations to take prompt, targeted actions, enhancing overall network security.
An LSTM-based prediction model for gradient-descending optimization in virtual learning environments Edi Ismanto; Noverta Effendi
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p199-207

Abstract

A virtual learning environment (VLE) is an online learning platform that allows many students, even millions, to study according to their interests without being limited by space and time. Online learning environments have many benefits, but they also have some drawbacks, such as high dropout rates, low engagement, and students' self-regulated behavior. Evaluating and analyzing the students' data generated from online learning platforms can help instructors to understand and monitor students learning progress. In this study, we suggest a predictive model for assessing student success in online learning. We investigate the effect of hyperparameters on the prediction of student learning outcomes in VLEs by the long short-term memory (LSTM) model. A hyperparameter is a parameter that has an impact on prediction results. Two optimization algorithms, adaptive moment estimation (Adam) and Nesterov-accelerated adaptive moment estimation (Nadam), were used to modify the LSTM model's hyperparameters. Based on the findings of research done on the optimization of the LSTM model using the Adam and Nadam algorithm. The average accuracy of the LSTM model using Nadam optimization is 89%, with a maximum accuracy of 93%. The LSTM model with Nadam optimisation performs better than the model with Adam optimisation when predicting students in online learning.
Agile adoption challenges in insurance: a systematic literature and expert review Krishna Yudhakusuma Putra Munandar; Teguh Raharjo
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p268-278

Abstract

The drawback of agile is struggled to function in large businesses like banks, insurance companies, and government agencies, which are frequently associated with cumbersome processes. Traditional software development techniques were cumbersome and pay more attention to standardization and industry, this leads to high costs and prolonged costs. The insurance company does not embrace change and agility may find themselves distracted and lose customers to agile competitors who are more relevant and customer-centric. Thus, to investigate the challenges and to recognize the prospect of agile adoption in insurance industry, a systematic literature review (SLR) in this study was organized and validated by expert review from professional with expertise in agile. The project performance domain from project management body of knowledge (PMBOK) was applied to align the challenges and the solution. Academicians and practitioners can acquire the perception and knowledge in having exceeded understanding about the challenge and solution of agile adoption from the results.
Development reference model to build management reporter using dynamics great plain aggregated Santo Fernandi Wijaya; Jansen Wiratama; Verri Kuswanto
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p240-248

Abstract

The digital technology transformation impacts changes in business patterns that require companies to innovate to act appropriately in making strategic decisions quickly, precisely, and accurately to increase efficiency, be practical company performance, and impacts changes in business patterns that require companies to innovate to act appropriately in making strategic decisions quickly to improve the performance. An enterprise resource planning (ERP) system is one step toward achieving performance. ERP system is one step to achieving performance. ERP system is essential for companies to automate the efficiency of business processes. The decisions from management in implementing the ERP system are necessary for ERP implementation to be successful. However, in practice, companies still experience complexity. For that, it needs to be considered related a business process reference model is essential to enhance efficiency in implementing the ERP used. This research discusses the business process reference model based on the ERP dynamics great plain (GP) application aggregated using management reporter (MR) to help users better understand the practical overview. The methodology utilizes a reference model based on Microsoft Dynamics GP guidelines with a business process redesign approach. This contributes to developing business processes to help users understand using the ERP dynamics GP application.
Collecting and analyzing network-based evidence K. Singh, Ashwini; Kamble, Dhwaniket; Bains, Abhishek; Tiwari, Naman; R. Deshmukh, Tejas; Pandey, Sanidhya; Kumar, Hemant; M. Bhalerao, Diksha
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p1-6

Abstract

Since nearly the beginning of the Internet, malware has been a significant deterrent to productivity for end users, both personal and business related. Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply ‘witness’ of a criminal event. Forensic investigations include collection, recovery, analysis, and presentation of information stored on network devices and related to network crimes. These activities often involve wide range of analysis tools and application of different methods. This work presents methods that helps digital investigators to correlate and present information acquired from forensic data, with the aim to get a more valuable reconstructions of events or action to reach case conclusions. Main aim of network forensic is to gather evidence. Additionally, the evidence obtained during the investigation must be produced through a rigorous investigation procedure in a legal context. 
Hybrid model for detection of brain tumor using convolution neural networks K., Bhagyalaxmi; Dwarakanath, B.
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p84-90

Abstract

The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.

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